SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 2051–2100 of 15113 papers

TitleStatusHype
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
Provably Efficient Online Hyperparameter Optimization with Population-Based BanditsCode1
Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline GenerationCode1
Multi Type Mean Field Reinforcement LearningCode1
Soft Hindsight Experience ReplayCode1
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision MakingCode1
Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning FrameworkCode1
Effective Diversity in Population Based Reinforcement LearningCode1
Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated DrivingCode1
Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine LearningCode1
Goal-directed graph construction using reinforcement learningCode1
PCGRL: Procedural Content Generation via Reinforcement LearningCode1
Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement LearningCode1
Graph Constrained Reinforcement Learning for Natural Language Action SpacesCode1
On Simple Reactive Neural Networks for Behaviour-Based Reinforcement LearningCode1
SARL*: Deep Reinforcement Learning based Human-Aware Navigation for Mobile Robot in Indoor EnvironmentsCode1
Discriminator Soft Actor Critic without Extrinsic RewardsCode1
Gradient Surgery for Multi-Task LearningCode1
Tree-Structured Policy based Progressive Reinforcement Learning for Temporally Language Grounding in VideoCode1
Lipschitz Lifelong Reinforcement LearningCode1
PoPS: Policy Pruning and Shrinking for Deep Reinforcement LearningCode1
GridMask Data AugmentationCode1
POPCORN: Partially Observed Prediction COnstrained ReiNforcement LearningCode1
Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation ErrorsCode1
Population-Guided Parallel Policy Search for Reinforcement LearningCode1
Reinforcement Learning via Fenchel-Rockafellar DualityCode1
Blue River Controls: A toolkit for Reinforcement Learning Control Systems on HardwareCode1
Deep Reinforcement Learning for Active Human Pose EstimationCode1
A Boolean Task Algebra for Reinforcement LearningCode1
Represented Value Function Approach for Large Scale Multi Agent Reinforcement LearningCode1
MushroomRL: Simplifying Reinforcement Learning ResearchCode1
Meta Reinforcement Learning with Autonomous Inference of Subtask DependenciesCode1
CURL: Contrastive Unsupervised Representation Learning for Reinforcement LearningCode1
An Optimistic Perspective on Offline Deep Reinforcement LearningCode1
Variational Imitation Learning with Diverse-quality DemonstrationsCode1
Bridging the Gap Between f-GANs and Wasserstein GANsCode1
Learning to Navigate in Synthetically Accessible Chemical Space Using Reinforcement LearningCode1
Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot ControlCode1
PAC Confidence Sets for Deep Neural Networks via Calibrated PredictionCode1
Pseudo Random Number Generation: a Reinforcement Learning approachCode1
Imitation Learning via Off-Policy Distribution MatchingCode1
VALAN: Vision and Language Agent NavigationCode1
Simplified Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
Dream to Control: Learning Behaviors by Latent ImaginationCode1
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement LearningCode1
Staying up to Date with Online Content Changes Using Reinforcement Learning for SchedulingCode1
ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization ProblemsCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
A Deep Reinforcement Learning Approach to First-Order Logic Theorem ProvingCode1
PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified